Geoscience Reference
In-Depth Information
Amherst, Massachusetts, winters I'd endured for the first eighteen years of life, but I was also
attracted by the school's reputation as one of the world's leading scientific research institutions. I
chose to major in physics, with a second major in applied math. The summer following my freshman
year, I began doing research in theoretical physics that emphasized the computational approaches to
problem solving I so enjoyed.
The project I was working on bore some resemblance to the tic-tac-toe problem that had
captivated me in high school; it too involved the concept of randomness. The project employed what
is known as a Monte Carlo method, named for its resemblance to a casino game—a method that I
would make use of years later in my climate research. Much as gamblers in Monaco's famous casino
town engage in random rolls of the dice in hope of monetary reward, scientists generate random
numbers on a computer in hope of simulating processes in nature that have a random component.
One example is the molecular interactions that govern the behavior of a solid or liquid. While
the fluctuations of the individual molecules are random in nature, external conditions—the ambient
temperature, in particular—influence the collective behavior of the molecules. The warmer the
temperature, for example, the more energetic the random fluctuations. Thus low temperatures favor
relatively ordered states (e.g., ice crystals), while high temperatures favor relatively disordered
states (e.g., water vapor). Shifts between these states are typically abrupt. There is a critical
temperature at which the system, when warmed, will suddenly undergo a phase transition from the
ordered state to the disordered state, or vice versa in the case of cooling. One can explore phase
transitions by representing the interactions between molecules in a computer model simulation,
generating random molecular perturbations in the model to mimic the real-world random fluctuations
of molecules.
I was using this type of Monte Carlo approach to investigate the theoretical behavior of liquid
crystals—the materials used in liquid crystal displays (LCDs) employed in laptops, TVs, and digital
watches. My research was aimed at determining how the critical temperature of the transition
between the ordered and disordered phases of liquid crystals might vary under different conditions.
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My adviser was a theoretical physical chemist named Tony Haymet, who much later—coincidentally
enough—went on to direct one of the world's premier climate research institutions, the Scripps
Institution for Oceanography at the University of California, San Diego.
When Tony left UC Berkeley a couple of years after my arrival, I continued my undergraduate
research with Didier de Fontaine, a professor of materials science studying the properties of an
exciting new material—a high-temperature superconductor. A superconductor is a material that
conducts an electric current with no resistance, a property with profound real-world applications
such as in the operation of super-fast bullet trains. Conventional (metallic) superconducting materials
need to be cooled nearly to a temperature of absolute zero, making them expensive to maintain. In the
mid-1980s, scientists discovered that certain ceramic materials had a remarkable property; they
super-conducted at much higher temperatures, above even the temperature of liquid nitrogen (a very
inexpensive coolant).
When I joined de Fontaine and his group in late 1987, they had been working for months to
model the behavior of just such a material: yttrium barium copper oxide (YBCO). They were using
Monte Carlo computer approaches similar to those I'd been using to study liquid crystals, so the
project was a natural fit. Over the next two years, I worked with de Fontaine's group on modeling the
transitions between ordered and disordered phases of YBCO. We were stymied by one vexing
